




'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {"vocab_file": "sentencepiece.model"}

UpperCAmelCase_					=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

UpperCAmelCase_					=   {
    "google/rembert": 2_56,
}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase="[CLS]"   ,				UpperCamelCase="[SEP]"   ,				UpperCamelCase="[UNK]"   ,				UpperCamelCase="[SEP]"   ,				UpperCamelCase="[PAD]"   ,				UpperCamelCase="[CLS]"   ,				UpperCamelCase="[MASK]"   ,				**UpperCamelCase   ,				)					->     Union[str, Any]:
     super().__init__(
         do_lower_case=UpperCamelCase   ,				remove_space=UpperCamelCase   ,				keep_accents=UpperCamelCase   ,				bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				sep_token=UpperCamelCase   ,				pad_token=UpperCamelCase   ,				cls_token=UpperCamelCase   ,				mask_token=UpperCamelCase   ,				**UpperCamelCase   ,				)

     __a								=       do_lower_case
     __a								=       remove_space
     __a								=       keep_accents
     __a								=       vocab_file

     __a								=       spm.SentencePieceProcessor()
     self.sp_model.Load(UpperCamelCase   )







   @property
   def 		UpperCamelCase__      (						self   )					->     Dict:
     return len(self.sp_model   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       {self.convert_ids_to_tokens(UpperCamelCase   ): i for i in range(self.vocab_size   )}
     vocab.update(self.added_tokens_encoder   )
     return vocab







   def __getstate__(						self   )					->     List[Any]:
     __a								=       self.__dict__.copy()
     __a								=       None
     return state







   def __setstate__(						self   ,				UpperCamelCase   )					->     Optional[int]:
     __a								=       d
     __a								=       spm.SentencePieceProcessor()
     self.sp_model.Load(self.vocab_file   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=False   )					->     str:
     __a								=       self.sp_model.EncodeAsPieces(UpperCamelCase   )
     return pieces







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[int]:
     return self.sp_model.PieceToId(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     List[Any]:
     return self.sp_model.IdToPiece(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     int:
     __a								=       self.sp_model.decode_pieces(UpperCamelCase   )
     return out_string







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     __a								=       [self.sep_token_id]
     __a								=       [self.cls_token_id]
     if token_ids_a is None:
       return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   ,				UpperCamelCase = False   )					->     List[int]:

     if already_has_special_tokens:
       if token_ids_a is not None:
         raise ValueError(
             'You should not supply a second sequence if the provided sequence of '
             'ids is already formatted with special tokens for the model.'   )
       return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
       return [1] + ([0] * len(UpperCamelCase   )) + [1] + ([0] * len(UpperCamelCase   )) + [1]
     return [1] + ([0] * len(UpperCamelCase   )) + [1]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     __a								=       [self.sep_token_id]
     __a								=       [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
     return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     Tuple[str]:
     if not os.path.isdir(UpperCamelCase   ):
       logger.error('Vocabulary path ({}) should be a directory'.format(UpperCamelCase   )   )
       return
     __a								=       os.path.join(
         UpperCamelCase   ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

     if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCamelCase   ):
       copyfile(self.vocab_file   ,				UpperCamelCase   )

     return (out_vocab_file,)









'''simple docstring'''


from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

UpperCAmelCase_					=   {
    "facebook/blenderbot_small-90M": 5_12,
}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   BlenderbotSmallTokenizer







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				**UpperCamelCase   ,				)					->     List[Any]:
     super().__init__(
         ByteLevelBPETokenizer(
             vocab=UpperCamelCase   ,				merges=UpperCamelCase   ,				add_prefix_space=UpperCamelCase   ,				trim_offsets=UpperCamelCase   ,				)   ,				bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       add_prefix_space







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     List[Any]:
     __a								=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     __a								=       [self.sep_token_id]
     __a								=       [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



